缩小采样区域的蒙特卡罗移动节点定位算法

Monte Carlo localization algorithm for mobile node by reducing sampled area

针对无线传感器网络中移动节点定位问题,提出一种适用于未知节点移动而信标节点固定的改进蒙特卡罗定位算法,充分利用信标节点与未知节点间的测距误差来缩小采样区域,提高采样效率。仿真结果表明,改进算法在信标节点密度、连通度和节点最大运动速度等不同情况下均能提高定位精度,减少采样次数和计算量,延长网络的生存周期。

In response to the problem of mobile node position for WSN, an improved MCL algorithm for mobile unknown node and fixed beacon node is proposed. Sampled area is reduced to improve the sampling efficiency by using the ranging error of beacon node and unknown node sufficiently. The simulation results show that the improved algorithm can improve the localization accuracy, reduce the number of sampling and computation, and prolong the network life cycle in different conditions of the beacon node density, connectivity and the maximum velocity of node.